International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Flower Identification Using Convolutional Neural Networks: An Intelligent Deep Learning Approach for Automated Flower Species Classification |
|---|---|
| ABSTRACT | Flower identification is an important task in agriculture, botany, horticulture, and environmental research. Manual identification of flower species requires botanical knowledge and is often time-consuming. This paper presents an intelligent flower identification system using Convolutional Neural Networks (CNN) to automatically classify flower images. The proposed system recognizes seven flower species, namely Rose, Lotus, Sunflower, Tulip, Daisy, Lily, and Dandelion. Image preprocessing techniques such as resizing and normalization are applied before training the CNN model to improve prediction performance. The trained model is integrated with a Flask-based web application that enables users to upload flower images and obtain prediction results with confidence scores. The system also provides botanical information, prediction history, and PDF report generation for future reference. Experimental results demonstrate that the proposed system achieves reliable classification accuracy and provides a simple, efficient, and user-friendly solution for automated flower species identification. |
| AUTHOR | A.NAGABHUSHANA, T.RAJESH Asst. Professor, Dept. of MCA, NSRIT, Visakhapatnam, AP, India Dept. of MCA, NSRIT, Visakhapatnam, AP, India |
| VOLUME | 186 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1407025 |
| pdf/25_Flower Identification Using Convolutional Neural Networks An Intelligent Deep Learning Approach for Automated Flower Species Classification.pdf | |
| KEYWORDS | |
| References | [1] Nurul Fatihah Sahidan, Ahmad Khairi Juha, Norasiah Mohammad, and Zaidah Ibrahim, "Flower and Leaf Recognition for Plant Identification Using Convolutional Neural Network," Indonesian Journal of Electrical Engineering and Computer Science, Vol. 16, No. 2, 2019. [2] M. Cıbuk, U. Budak, Y. Guo, M. C. Ince, and A. Sengur, "Efficient Deep Features Selections and Classification for Flower Species Recognition," Measurement, Vol. 137, 2019. [3] Mesut Toğaçar, Burhan Ergen, and Zafer Cömert, "Classification of Flower Species Using Deep Learning Features," Measurement, Vol. 158, 2020. [4] Touqeer Abbas, Abdul Razzaq, Muhammad Azam Zia, and Shikali Casper, "Deep Neural Networks for Automatic Flower Species Localization and Recognition," Computational Intelligence and Neuroscience, 2022. [5] Muhammed Yildirim, Ahmet Cinar, and Emine Cengil, "Flower Identification and Quality Assessment Using Machine Learning and CNN," Grenze International Journal of Engineering and Technology, Vol. 10, No. 2, 2024. [6] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Communications of the ACM, Vol. 60, No. 6, 2017. [7] Karen Simonyan and Andrew Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," International Conference on Learning Representations (ICLR), 2015. [8] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, "Deep Residual Learning for Image Recognition," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. [9] Christian Szegedy, Wei Liu, Yangqing Jia, et al., "Going Deeper with Convolutions," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. [10] François Chollet, "Xception: Deep Learning with Depthwise Separable Convolutions," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. [11] Mingxing Tan and Quoc V. Le, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks," International Conference on Machine Learning (ICML), 2019. [12] M. Turkoglu, U. Budak, Y. Guo, and A. Sengur, "A Multi-Division Convolutional Neural Network-Based Plant Recognition System," Applied Sciences, Vol. 11, 2021. [13] "Research on Plant Species Identification Based on Improved Convolutional Neural Network," Phyton International Journal of Experimental Botany, Vol. 92, 2023. [14] Philipe A. Dias, Amy Tabb, and Henry Medeiros, "Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network," 2018. [15] Mustafa Yurdakul, Enes Ayan, Fahrettin Horasan, and Sakir Tasdemir, "A Mobile Application for Flower Recognition System Based on Convolutional Neural Networks," 2026. |
